Option pricing using high-frequency futures prices

2021 ◽  
Author(s):  
Stavros Degiannakis ◽  
Christos Floros ◽  
Thomas Poufinas ◽  
George Filis ◽  
Konstantinos Gkillas
Keyword(s):  
Option Pricing ◽  
High Frequency ◽  
Futures Prices

scholarly journals An Option Pricing Model Using High Frequency Data

2016 ◽  
Vol 91 ◽  
pp. 175-179
Author(s):  
Saebom Jeon ◽  
Won Chang ◽  
Yousung Park
Keyword(s):  
Option Pricing ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Pricing Model ◽  
Option Pricing Model

The Informational Content of High-Frequency Option Prices

2021 ◽  
Author(s):  
Diego Amaya ◽  
Jean-François Bégin ◽  
Geneviève Gauthier
Keyword(s):  
Option Pricing ◽  
High Frequency ◽  
Parameter Estimates ◽  
Realized Variance ◽  
Risk Premiums ◽  
Additional Information ◽  
Option Returns ◽  
Option Pricing Model ◽  
Total Variability ◽  
Variance Risk

We propose the option realized variance as an observable variable to summarize the information from high-frequency option data. This variable aggregates intraday option returns from midquote prices to compute an option’s total variability for a given day, providing additional information about the jump activity in the data generating process. Using the S&P 500 index time series and options data, this paper documents the performance of this realized measure in predicting the index realized variance as well as the equity and variance risk premiums. We estimate an option pricing model and analyze its parameter estimates. Our results show that excluding high-frequency option information produces significant differences in variance jump parameters, estimated risk premiums, and option pricing errors. This paper was accepted by Tyler Shumway, finance.

Option Pricing: The Choice of High Frequency Volatility Matters

2013 ◽  
Author(s):  
Patrick Geyer ◽  
Dimitris Karyampas
Keyword(s):  
Option Pricing ◽  
High Frequency

scholarly journals Chinese Stock Index Futures Price Fluctuation Analysis and Prediction Based on Complementary Ensemble Empirical Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Ruoyang Chen ◽  
Bin Pan
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Stock Index ◽  
Stock Index Futures ◽  
Futures Prices ◽  
Index Futures ◽  
Price Fluctuations ◽  
Mode Decomposition ◽  
Svm Model

Since the CSI 300 index futures officially began trading on April 15, 2010, analysis and predictions of the price fluctuations of Chinese stock index futures prices have become a popular area of active research. In this paper, the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method is used to decompose the sequences of Chinese stock index futures prices into residue terms, low-frequency terms, and high-frequency terms to reveal the fluctuation characteristics over different time scales of the sequences. Then, the CEEMD method is combined with the Particle Swarm Optimization (PSO) algorithm-based Support Vector Machine (SVM) model to forecast Chinese stock index futures prices. The empirical results show that the residue term determines the long-term trend of stock index futures prices. The low-frequency term, which represents medium-term price fluctuations, is mainly affected by policy regulations under the analysis of the Iterated Cumulative Sums of Squares (ICSS) algorithm, whereas short-term market disequilibrium, which is represented by the high-frequency term, plays an important local role in stock index futures price fluctuations. In addition, in forecasting the daily or even intraday price data of Chinese stock index futures, the combination prediction model is superior to the single SVM model, which implies that the accuracy of predicting Chinese stock index futures prices will be improved by considering fluctuation characteristics in different time scales.

scholarly journals Subordinated Binomial Option Pricing with Stochastic Arrival Intensity and Untraded Underlying Asset

2017 ◽  
Vol 6 (2) ◽  
pp. 190
Author(s):  
Carolyn W. Chang ◽  
Jack S.K. Chang
Keyword(s):  
Option Pricing ◽  
Original Model ◽  
Intensity Level ◽  
Futures Prices ◽  
Underlying Asset ◽  
Vix Options ◽  
Out Of Sample ◽  
Pricing Error ◽  
Option Pricing Model ◽  
Arrival Intensity

We extend the subordinated binomial option pricing model with stochastic arrival intensity (Chang, Chang and Lu, 2010) to allow for untraded underlying assets by using matching futures prices to imply out the underlying asset values. We empirically apply the model to VIX option pricing vis-à-vis the original model with constant arrival intensity (Chang, Chang and Tian, 2006) using a two-year set of daily VIX options and futures data to specifically examine the efficacy of adding stochastic arrival intensity and untraded underlying assets.  We find that the extended version significantly outperforms the original model both in sample and out-of-sample in terms of the MSE, with pricing error reduction about 37% and 32%, respectively, and additionally the outperformance is robust to the selection of the constant arrival intensity level.  We attribute the outperformance to the extended model’s incorporation of the stylized effects of mean-reversion and clustering in intensity arrivals as well as of the information content conveyed by the matching futures prices.

VOLATILITY AND LIQUIDITY ON HIGH-FREQUENCY ELECTRICITY FUTURES MARKETS: EMPIRICAL ANALYSIS AND STOCHASTIC MODELING

2020 ◽  
Vol 23 (04) ◽  
pp. 2050027
Author(s):  
MARCEL KREMER ◽  
FRED ESPEN BENTH ◽  
BJÖRN FELTEN ◽  
RÜDIGER KIESEL
Keyword(s):  
Monte Carlo ◽  
Stochastic Models ◽  
Continuous Time ◽  
High Frequency ◽  
Futures Markets ◽  
A Priori ◽  
Futures Market ◽  
Futures Prices ◽  
Volatility Function ◽  
The Relationship

This paper investigates the relationship between volatility and liquidity on the German electricity futures market based on high-frequency intraday prices. We estimate volatility by the time-weighted realized variance acknowledging that empirical intraday prices are not equally spaced in time. Empirical evidence suggests that volatility of electricity futures decreases as time approaches maturity, while coincidently liquidity increases. Established continuous-time stochastic models for electricity futures prices involve a growing volatility function in time and are thus not able to capture our empirical findings a priori. In Monte Carlo simulations, we demonstrate that incorporating increasing liquidity into the established models is key to model the decreasing volatility evolution.

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